Advanced Technology

Data Science

Collect, clean, and analyze data to support decisions in government, NGOs, and business.

Level: Beginner–Advanced Beginner: 4 weeks • Intermediate: 8 weeks • Advanced: 12 weeks Contact for pricing

Prerequisites

No prior experience required — open to beginners.

Certificate

Awarded by Rescue Academy on successful completion of the program's assessments and final project.

Learn Online — Live Classes

Register, receive your schedule, and join live instructor-led Data Science classes on Zoom. Assignments, instructor feedback, and a certificate on completion.

Learn In Person

Attend Data Science classes in person in Juba with hands-on labs, instructor mentorship, and a certificate on completion.

What You'll Learn

The data science life cycle and framing decision-linked questions
Exploratory analysis, statistics and uncertainty
Visualisation, storytelling and privacy-protecting outputs
Survey data, wrangling, inference and regression
Reproducible reporting, indicators and dashboards
Causal inference, predictive targeting, spatial and text data
Data governance and responsible data sharing

Program Objectives

Turn organisational questions into rigorous, decision-focused analyses
Collect, clean and combine real-world data, including survey data
Apply statistical, causal, predictive, spatial and text methods appropriately
Communicate findings and uncertainty clearly to decision-makers
Protect people's data and keep analyses reproducible and governed

Who It's For

M&E, research and data officers in NGOs and government
Analysts who want to move beyond spreadsheets
Graduates preparing for data science roles

Where You'll Use It

Monitoring and evaluation of humanitarian and development programmes
Public health, education and agriculture analysis
Business and market analysis

Curriculum

Instructor-led, practical training. Each level is a block of 4, 8 or 12 weeks; every module ends with a practical activity and an assessment, and every level with a project.

Beginner

4 weeks · 52 guided hours · 5 modules Free taster lessons available Level overview

Module 1: The Data Science Process 10 h

See how data science turns questions into evidence for decisions, where data comes from and the tools used in this program. 3 lessons

Module 2: Exploratory Data Analysis with pandas 11 h

Summarise, group and visualise data to find patterns, problems and questions worth investigating. 3 lessons

Module 3: Statistics for Decisions 10 h

Describe data with the right summary statistics, understand distributions and sampling, and explain uncertainty in plain language. 3 lessons

Module 4: Visualisation and Data Storytelling 11 h

Choose effective charts, build them in Python and turn analysis into a clear, honest story for decision-makers. 3 lessons

Module 5: Data Privacy and Ethics in Analysis 10 h

Protect the people behind the data when analysing, publishing and sharing results. 3 lessons

Level project: Water Access Snapshot from Survey Data

Intermediate

8 weeks · 90 guided hours · 6 modules Level overview

Module 1: Data Collection and Survey Data 15 h

Design questionnaires and samples, collect data digitally and prepare survey data correctly, including weights. 3 lessons

Module 2: Data Wrangling with pandas and NumPy 16 h

Reshape, merge and transform data from several sources efficiently and reliably. 3 lessons

Module 3: Statistical Inference 16 h

Estimate population values with confidence intervals, test hypotheses and interpret results without common mistakes. 3 lessons

Module 4: Relationships and Regression for Analysis 13 h

Describe relationships with correlation and regression, control for other factors and recognise confounding. 3 lessons

Module 5: Reporting and Dashboards 13 h

Produce reproducible reports and monitoring dashboards that stay correct as data is updated. 3 lessons

Module 6: Intermediate Data Science Project 17 h

Deliver the education programme monitoring analysis end to end, from merged data to report, dashboard and presentation. 3 lessons

Level project: Education Programme Monitoring Analysis

Advanced

12 weeks · 140 guided hours · 6 modules Level overview

Module 1: Causal Inference and Programme Evaluation 22 h

Move from association to cause with experiments and quasi-experimental designs, and understand their assumptions. 4 lessons

Module 2: Predictive Modelling in Data Science Projects 22 h

Use predictive models where they help a data science project, evaluate them for the decision at hand and communicate their outputs responsibly. 4 lessons

Module 3: Working with Larger and Spatial Data 22 h

Handle data too large for simple workflows, query it with SQL and analyse and map location data. 4 lessons

Module 4: Text Data and Open-Ended Responses 20 h

Analyse open-ended survey answers, feedback and documents with text methods, including careful use of AI tools. 4 lessons

Module 5: Data Governance and Responsible Data Science 24 h

Manage data quality, documentation, reproducibility and responsible sharing so that analyses can be trusted. 4 lessons

Module 6: Data Science Capstone 30 h

Lead the cash assistance impact and targeting analysis from questions to a decision memo, combining causal, spatial, predictive and text methods responsibly. 5 lessons

Level project: Impact and Targeting Analysis for a Cash Assistance Programme

Final practical project

Impact and Targeting Analysis for a Cash Assistance Programme

A multi-method analysis that estimates a programme's effect, maps needs, evaluates targeting, analyses beneficiary feedback and recommends changes in a decision memo.

Deliverables

A reproducible repository with documented data and analyses
Causal, spatial, predictive and text analyses
A decision memo with recommendations and uncertainty
A technical appendix and a presentation

Assessed on

Methods suit each question and their assumptions are checked
Findings are triangulated and clearly communicated
Privacy and governance requirements are met throughout
Every number can be regenerated from the raw data

Worked Examples

A selection of the visual, practical examples used in this program's lessons — each teaches a different skill.

Free Course: Data Science Starter

A free introduction to data science: the project lifecycle, exploring a dataset, basic statistical reasoning, visual storytelling and handling personal data responsibly. Free, self-study, about 6 hours — no certificate. The full program above is taught live by an instructor, with practical assignments, assessments and a certificate.

Open the free course

Tools & Technologies

Primary Tools

Python with Jupyter or Google Colab — The analysis environment for every module Practice: Reproducible notebooks and scripts
pandas and NumPy — Loading, cleaning, reshaping and summarising data Practice: Data wrangling and exploratory analysis
matplotlib and seaborn — Charts for exploration and communication Practice: Clear, honest visualisation

Supporting Tools

SciPy and statsmodels — Statistical tests, confidence intervals and regression Practice: Inference and explanatory modelling
KoboToolbox or ODK — Digital survey design and collection Practice: Better data at the source
Git — Version control for analysis projects Practice: Reproducibility and review

Professional / Industry Tools

scikit-learn — Predictive models used within projects at Advanced level Practice: Targeting models and evaluation
DuckDB and GeoPandas — Large-file SQL and spatial analysis Practice: Efficient querying and mapping
A BI tool (e.g. Power BI or Looker Studio) or Streamlit — Dashboards for monitoring Practice: Dashboard design for decision-makers

Career Opportunities

Data/AI analyst role at a bank, telecom, or NGO
Research or further-study pathway in AI/ML/Data Science
Remote/freelance AI or data work

Practical Projects

Complete a full data analysis: clean, explore, visualize, and report insights from a real dataset
Build a simple data dashboard summarizing key findings for a non-technical audience
Take a completed analysis and produce a one-page, non-technical summary with a single clear recommendation for a decision-maker who has two minutes to read it

Problem-Solving Focus

Problem: A real-world dataset (survey responses, sensor logs, program records) has missing, duplicate, or inconsistently formatted entries

Approach: Systematically detect and handle missing or duplicate data — through imputation, removal, or explicit flagging — rather than dropping rows blindly, and document every decision made along the way.

Problem: Two variables are correlated in the data, and someone wants to conclude that one causes the other

Approach: Distinguish correlation from causation explicitly, and design any conclusion or recommendation around what the data can actually prove — not what would make the best headline.

Problem: A non-technical decision-maker — a director, funder, or government official — needs to act on the analysis but won't read a table of statistics

Approach: Translate the findings into a clear visual story and one specific recommendation, not a dump of every chart and number the analysis produced.

Problem: Sensitive personal data (health status, income, location) is required for the analysis, but sharing it directly risks people's privacy

Approach: Anonymize or aggregate the data before analysis or sharing, applying the same privacy practices covered directly in this program's Data Privacy module.

Ready to register for Data Science?